Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules
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The aim of this study was to develop an ultrasound-based nomogram to improve the diagnostic accuracy of the identification of malignant thyroid nodules.
A total of 1675 histologically proven thyroid nodules (1169 benign, 506 malignant) were included in this study. The nodules were grouped into the training dataset (n = 700), internal validation dataset (n = 479), or external validation dataset (n = 496). The grayscale ultrasound features included the nodule size, shape, aspect ratio, echogenicity, margins, and calcification pattern. We applied least absolute shrinkage and selection operator (lasso) regression to select the strongest features for the nomogram. Nomogram discrimination (area under the receiver operating characteristic curve, AUC) and calibration were assessed. The nomogram was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate a mean AUC and 95% confidence interval (CI).
The nomogram showed good discrimination in the training dataset, with an AUC of 0.936 (95% CI: 0.918–0.953) and good calibration. Application of the nomogram to the internal validation dataset also resulted in good discrimination (AUC: 0.935; 95% CI, 0.915–0.954) and good calibration. The model tested in an external validation dataset demonstrated a lower AUC of 0.782 (95% CI: 0.776–0.789).
This ultrasound-based nomogram can be used to quantify the probability of malignant thyroid nodules.
• Ultrasound examination is helpful in the differential diagnosis of malignant and benign thyroid nodules.
• However, ultrasound accuracy relies heavily on examiner experience.
• A less subjective diagnostic model is desired, and the developed nomogram for thyroid nodules showed good discrimination and good calibration.
KeywordsThyroid nodule Ultrasonography Diagnosis Nomogram Area Under Curve
Area under the curve
Least absolute shrinkage and selection operator
This study has received funding from the science and technology project of Foshan (2017AB003623 and 2017AB003683).
Compliance with ethical standards
The scientific guarantor of this publication is Qiugen Hu
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• diagnostic study
• performed at two institutions
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